Various multiuser detection algorithms have been proposed to detect and distinguish simultaneous user data streams included in 3GPP long term evolution (“LTE”) telecommunication uplinks. These approaches can be broadly categorized into linear and nonlinear multiuser detection.
Linear Multi-User Detection (“MUD”) methods that are used for multiuser detection in an interference limited environment, such as an LTE uplink, include linear MMSE and Zero Forcing. Non-linear multiuser detection methods include MMSE, successive or parallel interference cancellation, and approximate Maximum Likelihood Detection (“MLD”).
In particular, approximate MLD is an attractive nonlinear MUD algorithm because of its relatively low computational complexity and its superior performance. To improve its performance still further, MLD can be used in a turbo loop, wherein the initial equalization is based on a low complexity linear MMSE-IRC, Linear MMSE, or zero forcing equalizer.
Approaches to implementing approximate MLD have focused on formulating and evaluating its performance under the assumption that the modeled users are corrupted only by Additive White Gaussian Noise (“AWGN”). Accordingly, these approaches typically include a whitened, matched pre-filter stage, such as the Maximum Probability (“MAP”) pre-filter 100 included in the receiver architecture of FIG. 1, which produces a maximum input signal to noise ratio for MLD when the residual noise is purely AWGN.
However, in practice uplink data also typically includes not Gaussian residual interference from users that are not modeled, and are therefore not taken into account during MLD. As a result, using only an AWGN-whitened matched pre-filter is not optimal in such cases, and can result in a decreased Signal to Interference and Noise Ratio (“SINR”) and to degraded performance of the MLD.
Additionally, for the MLD algorithms that are typically applied to LTE uplink data, it can be difficult to distinguish between desired, significant users that should be modeled (e.g. high SINR users) and insignificant (e.g. low SINR) interfering users that should be suppressed. As a result, such MLD algorithms typically model low SINR users as well as high SINR users, and this can result in performance that is worse than if the low SINR users had been suppressed.
What is needed, therefore, is a method and system for applying a whitening, matched pre-filter to multi-user uplink data that produces a MLD input signal with maximum SINR even when the signals from modeled users are corrupted by residual interference from non-modeled users as well as AWGN.